Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations6325
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory741.2 KiB
Average record size in memory120.0 B

Variable types

Numeric9
Categorical4
Boolean1

Alerts

age_of_first_emp is highly overall correlated with cb_person_cred_hist_length and 2 other fieldsHigh correlation
cb_person_cred_hist_length is highly overall correlated with age_of_first_emp and 1 other fieldsHigh correlation
cb_person_default_on_file is highly overall correlated with loan_grade and 1 other fieldsHigh correlation
loan_amnt is highly overall correlated with loan_percent_incomeHigh correlation
loan_grade is highly overall correlated with cb_person_default_on_file and 1 other fieldsHigh correlation
loan_int_rate is highly overall correlated with cb_person_default_on_file and 1 other fieldsHigh correlation
loan_percent_income is highly overall correlated with loan_amntHigh correlation
person_age is highly overall correlated with age_of_first_emp and 1 other fieldsHigh correlation
person_emp_length is highly overall correlated with age_of_first_empHigh correlation
id has unique values Unique
person_emp_length has 837 (13.2%) zeros Zeros

Reproduction

Analysis started2025-03-11 17:37:15.617006
Analysis finished2025-03-11 17:37:24.961975
Duration9.34 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct6325
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29261.091
Minimum7
Maximum58640
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2025-03-11T13:37:25.051674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile2956.4
Q114932
median29383
Q343728
95-th percentile55345.2
Maximum58640
Range58633
Interquartile range (IQR)28796

Descriptive statistics

Standard deviation16689.773
Coefficient of variation (CV)0.57037427
Kurtosis-1.1799158
Mean29261.091
Median Absolute Deviation (MAD)14427
Skewness-0.0005590279
Sum1.850764 × 108
Variance2.7854853 × 108
MonotonicityStrictly increasing
2025-03-11T13:37:25.172980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 1
 
< 0.1%
38914 1
 
< 0.1%
38854 1
 
< 0.1%
38852 1
 
< 0.1%
38847 1
 
< 0.1%
38845 1
 
< 0.1%
38839 1
 
< 0.1%
38834 1
 
< 0.1%
38804 1
 
< 0.1%
38793 1
 
< 0.1%
Other values (6315) 6315
99.8%
ValueCountFrequency (%)
7 1
< 0.1%
10 1
< 0.1%
15 1
< 0.1%
32 1
< 0.1%
37 1
< 0.1%
38 1
< 0.1%
62 1
< 0.1%
68 1
< 0.1%
69 1
< 0.1%
73 1
< 0.1%
ValueCountFrequency (%)
58640 1
< 0.1%
58621 1
< 0.1%
58611 1
< 0.1%
58563 1
< 0.1%
58549 1
< 0.1%
58546 1
< 0.1%
58532 1
< 0.1%
58515 1
< 0.1%
58498 1
< 0.1%
58492 1
< 0.1%

person_age
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.608854
Minimum20
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2025-03-11T13:37:25.304328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q123
median26
Q330
95-th percentile40
Maximum76
Range56
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.112149
Coefficient of variation (CV)0.22138366
Kurtosis5.4637559
Mean27.608854
Median Absolute Deviation (MAD)3
Skewness1.912348
Sum174626
Variance37.358366
MonotonicityNot monotonic
2025-03-11T13:37:25.626226image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
23 813
12.9%
22 769
12.2%
24 697
11.0%
25 564
 
8.9%
27 426
 
6.7%
26 423
 
6.7%
28 402
 
6.4%
29 350
 
5.5%
30 238
 
3.8%
31 212
 
3.4%
Other values (36) 1431
22.6%
ValueCountFrequency (%)
20 4
 
0.1%
21 194
 
3.1%
22 769
12.2%
23 813
12.9%
24 697
11.0%
25 564
8.9%
26 423
6.7%
27 426
6.7%
28 402
6.4%
29 350
5.5%
ValueCountFrequency (%)
76 1
 
< 0.1%
73 2
 
< 0.1%
66 2
 
< 0.1%
65 2
 
< 0.1%
64 1
 
< 0.1%
62 1
 
< 0.1%
60 2
 
< 0.1%
58 7
0.1%
57 2
 
< 0.1%
56 1
 
< 0.1%

person_income
Real number (ℝ)

Distinct1005
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63778.993
Minimum4200
Maximum1839784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2025-03-11T13:37:25.750482image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4200
5-th percentile26000
Q140000
median56000
Q376500
95-th percentile120000
Maximum1839784
Range1835584
Interquartile range (IQR)36500

Descriptive statistics

Standard deviation55045.583
Coefficient of variation (CV)0.86306761
Kurtosis411.26173
Mean63778.993
Median Absolute Deviation (MAD)17000
Skewness15.658839
Sum4.0340213 × 108
Variance3.0300162 × 109
MonotonicityNot monotonic
2025-03-11T13:37:25.893559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000 460
 
7.3%
80000 343
 
5.4%
60000 260
 
4.1%
48000 153
 
2.4%
50000 147
 
2.3%
30000 141
 
2.2%
70000 126
 
2.0%
45000 115
 
1.8%
120000 103
 
1.6%
75000 98
 
1.5%
Other values (995) 4379
69.2%
ValueCountFrequency (%)
4200 1
 
< 0.1%
5000 1
 
< 0.1%
9600 6
0.1%
10000 1
 
< 0.1%
12000 11
0.2%
12996 1
 
< 0.1%
13000 1
 
< 0.1%
14400 14
0.2%
15000 6
0.1%
15120 1
 
< 0.1%
ValueCountFrequency (%)
1839784 1
< 0.1%
1824000 1
< 0.1%
1200000 1
< 0.1%
948000 1
< 0.1%
928000 1
< 0.1%
900000 1
< 0.1%
889000 1
< 0.1%
828000 1
< 0.1%
612000 1
< 0.1%
510000 1
< 0.1%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.8 KiB
RENT
3320 
MORTGAGE
2631 
OWN
366 
OTHER
 
8

Length

Max length8
Median length4
Mean length5.6072727
Min length3

Characters and Unicode

Total characters35466
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowMORTGAGE
3rd rowOWN
4th rowRENT
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
RENT 3320
52.5%
MORTGAGE 2631
41.6%
OWN 366
 
5.8%
OTHER 8
 
0.1%

Length

2025-03-11T13:37:26.023814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:37:26.153127image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
rent 3320
52.5%
mortgage 2631
41.6%
own 366
 
5.8%
other 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R 5959
16.8%
E 5959
16.8%
T 5959
16.8%
G 5262
14.8%
N 3686
10.4%
O 3005
8.5%
M 2631
7.4%
A 2631
7.4%
W 366
 
1.0%
H 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35466
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 5959
16.8%
E 5959
16.8%
T 5959
16.8%
G 5262
14.8%
N 3686
10.4%
O 3005
8.5%
M 2631
7.4%
A 2631
7.4%
W 366
 
1.0%
H 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35466
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 5959
16.8%
E 5959
16.8%
T 5959
16.8%
G 5262
14.8%
N 3686
10.4%
O 3005
8.5%
M 2631
7.4%
A 2631
7.4%
W 366
 
1.0%
H 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35466
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 5959
16.8%
E 5959
16.8%
T 5959
16.8%
G 5262
14.8%
N 3686
10.4%
O 3005
8.5%
M 2631
7.4%
A 2631
7.4%
W 366
 
1.0%
H 8
 
< 0.1%

person_emp_length
Real number (ℝ)

High correlation  Zeros 

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7459289
Minimum0
Maximum41
Zeros837
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2025-03-11T13:37:26.566673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile12
Maximum41
Range41
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0339857
Coefficient of variation (CV)0.84998865
Kurtosis2.7371902
Mean4.7459289
Median Absolute Deviation (MAD)3
Skewness1.2767834
Sum30018
Variance16.27304
MonotonicityNot monotonic
2025-03-11T13:37:26.988976image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 837
13.2%
2 788
12.5%
3 696
11.0%
1 581
9.2%
5 566
8.9%
4 562
8.9%
6 531
8.4%
7 454
7.2%
8 343
5.4%
9 244
 
3.9%
Other values (19) 723
11.4%
ValueCountFrequency (%)
0 837
13.2%
1 581
9.2%
2 788
12.5%
3 696
11.0%
4 562
8.9%
5 566
8.9%
6 531
8.4%
7 454
7.2%
8 343
5.4%
9 244
 
3.9%
ValueCountFrequency (%)
41 1
 
< 0.1%
27 1
 
< 0.1%
26 3
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 3
 
< 0.1%
22 4
 
0.1%
21 7
0.1%
20 11
0.2%
19 11
0.2%

loan_intent
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.8 KiB
MEDICAL
1412 
EDUCATION
1240 
VENTURE
1057 
PERSONAL
1013 
DEBTCONSOLIDATION
916 

Length

Max length17
Median length15
Mean length9.8694071
Min length7

Characters and Unicode

Total characters62424
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPERSONAL
2nd rowVENTURE
3rd rowMEDICAL
4th rowMEDICAL
5th rowPERSONAL

Common Values

ValueCountFrequency (%)
MEDICAL 1412
22.3%
EDUCATION 1240
19.6%
VENTURE 1057
16.7%
PERSONAL 1013
16.0%
DEBTCONSOLIDATION 916
14.5%
HOMEIMPROVEMENT 687
10.9%

Length

2025-03-11T13:37:27.097940image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:37:27.204958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
medical 1412
22.3%
education 1240
19.6%
venture 1057
16.7%
personal 1013
16.0%
debtconsolidation 916
14.5%
homeimprovement 687
10.9%

Most occurring characters

ValueCountFrequency (%)
E 8756
14.0%
O 6375
10.2%
N 5829
9.3%
I 5171
8.3%
T 4816
7.7%
A 4581
 
7.3%
D 4484
 
7.2%
C 3568
 
5.7%
M 3473
 
5.6%
L 3341
 
5.4%
Other values (7) 12030
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 8756
14.0%
O 6375
10.2%
N 5829
9.3%
I 5171
8.3%
T 4816
7.7%
A 4581
 
7.3%
D 4484
 
7.2%
C 3568
 
5.7%
M 3473
 
5.6%
L 3341
 
5.4%
Other values (7) 12030
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 8756
14.0%
O 6375
10.2%
N 5829
9.3%
I 5171
8.3%
T 4816
7.7%
A 4581
 
7.3%
D 4484
 
7.2%
C 3568
 
5.7%
M 3473
 
5.6%
L 3341
 
5.4%
Other values (7) 12030
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 8756
14.0%
O 6375
10.2%
N 5829
9.3%
I 5171
8.3%
T 4816
7.7%
A 4581
 
7.3%
D 4484
 
7.2%
C 3568
 
5.7%
M 3473
 
5.6%
L 3341
 
5.4%
Other values (7) 12030
19.3%

loan_grade
Categorical

High correlation 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size98.8 KiB
A
2048 
B
2026 
C
1159 
D
837 
E
210 
Other values (2)
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6325
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowB
3rd rowA
4th rowB
5th rowC

Common Values

ValueCountFrequency (%)
A 2048
32.4%
B 2026
32.0%
C 1159
18.3%
D 837
13.2%
E 210
 
3.3%
F 40
 
0.6%
G 5
 
0.1%

Length

2025-03-11T13:37:27.321245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:37:27.413100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
a 2048
32.4%
b 2026
32.0%
c 1159
18.3%
d 837
13.2%
e 210
 
3.3%
f 40
 
0.6%
g 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 2048
32.4%
B 2026
32.0%
C 1159
18.3%
D 837
13.2%
E 210
 
3.3%
F 40
 
0.6%
G 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6325
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2048
32.4%
B 2026
32.0%
C 1159
18.3%
D 837
13.2%
E 210
 
3.3%
F 40
 
0.6%
G 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6325
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2048
32.4%
B 2026
32.0%
C 1159
18.3%
D 837
13.2%
E 210
 
3.3%
F 40
 
0.6%
G 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6325
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2048
32.4%
B 2026
32.0%
C 1159
18.3%
D 837
13.2%
E 210
 
3.3%
F 40
 
0.6%
G 5
 
0.1%

loan_amnt
Real number (ℝ)

High correlation 

Distinct337
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10624.827
Minimum1000
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2025-03-11T13:37:27.529054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3000
Q16000
median9600
Q314500
95-th percentile24000
Maximum35000
Range34000
Interquartile range (IQR)8500

Descriptive statistics

Standard deviation6263.5418
Coefficient of variation (CV)0.5895194
Kurtosis0.86761122
Mean10624.827
Median Absolute Deviation (MAD)4400
Skewness0.96976248
Sum67202032
Variance39231956
MonotonicityNot monotonic
2025-03-11T13:37:27.663267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 460
 
7.3%
5000 453
 
7.2%
6000 360
 
5.7%
15000 322
 
5.1%
12000 280
 
4.4%
3000 219
 
3.5%
8000 206
 
3.3%
20000 195
 
3.1%
7000 194
 
3.1%
9000 165
 
2.6%
Other values (327) 3471
54.9%
ValueCountFrequency (%)
1000 45
0.7%
1075 1
 
< 0.1%
1150 1
 
< 0.1%
1200 17
 
0.3%
1300 1
 
< 0.1%
1350 2
 
< 0.1%
1400 4
 
0.1%
1450 2
 
< 0.1%
1500 37
0.6%
1600 7
 
0.1%
ValueCountFrequency (%)
35000 36
0.6%
33000 1
 
< 0.1%
31000 1
 
< 0.1%
30000 28
0.4%
28250 1
 
< 0.1%
28000 12
 
0.2%
27800 1
 
< 0.1%
27250 1
 
< 0.1%
27050 1
 
< 0.1%
27000 2
 
< 0.1%

loan_int_rate
Real number (ℝ)

High correlation 

Distinct265
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.12824
Minimum5.42
Maximum23.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2025-03-11T13:37:27.795768image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.03
Q17.9
median11.11
Q313.49
95-th percentile16.35
Maximum23.06
Range17.64
Interquartile range (IQR)5.59

Descriptive statistics

Standard deviation3.2496083
Coefficient of variation (CV)0.29201457
Kurtosis-0.7690113
Mean11.12824
Median Absolute Deviation (MAD)2.68
Skewness0.15910106
Sum70386.12
Variance10.559954
MonotonicityNot monotonic
2025-03-11T13:37:27.920057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.99 192
 
3.0%
7.51 174
 
2.8%
7.88 165
 
2.6%
7.49 160
 
2.5%
7.9 132
 
2.1%
13.49 119
 
1.9%
6.62 114
 
1.8%
6.03 105
 
1.7%
11.49 105
 
1.7%
5.42 98
 
1.5%
Other values (255) 4961
78.4%
ValueCountFrequency (%)
5.42 98
1.5%
5.79 80
1.3%
5.99 58
0.9%
6.03 105
1.7%
6.17 40
 
0.6%
6.39 7
 
0.1%
6.54 56
0.9%
6.62 114
1.8%
6.76 27
 
0.4%
6.91 54
0.9%
ValueCountFrequency (%)
23.06 1
< 0.1%
21.36 2
< 0.1%
21.21 1
< 0.1%
20.89 2
< 0.1%
20.62 1
< 0.1%
20.52 1
< 0.1%
20.48 1
< 0.1%
20.25 1
< 0.1%
20.17 1
< 0.1%
20.16 1
< 0.1%

loan_percent_income
Real number (ℝ)

High correlation 

Distinct56
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18814609
Minimum0
Maximum0.56
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2025-03-11T13:37:28.043072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.11
median0.17
Q30.26
95-th percentile0.39
Maximum0.56
Range0.56
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.1046068
Coefficient of variation (CV)0.55598713
Kurtosis-0.10094991
Mean0.18814609
Median Absolute Deviation (MAD)0.07
Skewness0.69345715
Sum1190.024
Variance0.010942583
MonotonicityNot monotonic
2025-03-11T13:37:28.161714image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 763
 
12.1%
0.07 378
 
6.0%
0.17 258
 
4.1%
0.23 239
 
3.8%
0.15 209
 
3.3%
0.19 193
 
3.1%
0.09 193
 
3.1%
0.11 180
 
2.8%
0.14 179
 
2.8%
0.16 178
 
2.8%
Other values (46) 3555
56.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.01 28
 
0.4%
0.02 16
 
0.3%
0.03 120
 
1.9%
0.04 96
 
1.5%
0.05 121
 
1.9%
0.06 155
2.5%
0.07 378
6.0%
0.08 160
2.5%
0.09 193
3.1%
ValueCountFrequency (%)
0.56 1
 
< 0.1%
0.53 2
 
< 0.1%
0.52 4
 
0.1%
0.51 9
 
0.1%
0.5 12
0.2%
0.49 10
 
0.2%
0.48 16
0.3%
0.47 16
0.3%
0.46 16
0.3%
0.45 26
0.4%

cb_person_default_on_file
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.6 KiB
False
5215 
True
1110 
ValueCountFrequency (%)
False 5215
82.5%
True 1110
 
17.5%
2025-03-11T13:37:28.263205image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

cb_person_cred_hist_length
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8093281
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2025-03-11T13:37:28.350044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q38
95-th percentile14
Maximum30
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0644719
Coefficient of variation (CV)0.69964579
Kurtosis3.6352869
Mean5.8093281
Median Absolute Deviation (MAD)2
Skewness1.6385602
Sum36744
Variance16.519932
MonotonicityNot monotonic
2025-03-11T13:37:28.457192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 1201
19.0%
4 1148
18.2%
3 1118
17.7%
8 380
 
6.0%
10 378
 
6.0%
9 375
 
5.9%
6 356
 
5.6%
5 356
 
5.6%
7 337
 
5.3%
14 115
 
1.8%
Other values (19) 561
8.9%
ValueCountFrequency (%)
2 1201
19.0%
3 1118
17.7%
4 1148
18.2%
5 356
 
5.6%
6 356
 
5.6%
7 337
 
5.3%
8 380
 
6.0%
9 375
 
5.9%
10 378
 
6.0%
11 88
 
1.4%
ValueCountFrequency (%)
30 3
 
< 0.1%
29 5
0.1%
28 5
0.1%
27 2
 
< 0.1%
26 5
0.1%
25 2
 
< 0.1%
24 8
0.1%
23 3
 
< 0.1%
22 7
0.1%
21 3
 
< 0.1%

loan_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size98.8 KiB
0
4777 
1
1548 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6325
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4777
75.5%
1 1548
 
24.5%

Length

2025-03-11T13:37:28.567971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-11T13:37:28.651794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 4777
75.5%
1 1548
 
24.5%

Most occurring characters

ValueCountFrequency (%)
0 4777
75.5%
1 1548
 
24.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6325
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4777
75.5%
1 1548
 
24.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6325
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4777
75.5%
1 1548
 
24.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6325
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4777
75.5%
1 1548
 
24.5%

age_of_first_emp
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.862925
Minimum14
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size98.8 KiB
2025-03-11T13:37:28.756388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile16
Q117
median22
Q326
95-th percentile36
Maximum74
Range60
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8142444
Coefficient of variation (CV)0.2980478
Kurtosis4.2137538
Mean22.862925
Median Absolute Deviation (MAD)4
Skewness1.5946833
Sum144608
Variance46.433927
MonotonicityNot monotonic
2025-03-11T13:37:28.882129image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1347
21.3%
22 472
 
7.5%
21 441
 
7.0%
23 439
 
6.9%
20 376
 
5.9%
24 354
 
5.6%
19 332
 
5.2%
25 318
 
5.0%
18 284
 
4.5%
26 238
 
3.8%
Other values (41) 1724
27.3%
ValueCountFrequency (%)
14 6
 
0.1%
15 101
 
1.6%
16 1347
21.3%
17 177
 
2.8%
18 284
 
4.5%
19 332
 
5.2%
20 376
 
5.9%
21 441
 
7.0%
22 472
 
7.5%
23 439
 
6.9%
ValueCountFrequency (%)
74 1
 
< 0.1%
70 1
 
< 0.1%
66 2
< 0.1%
65 1
 
< 0.1%
63 3
< 0.1%
60 1
 
< 0.1%
58 1
 
< 0.1%
57 2
< 0.1%
56 2
< 0.1%
55 3
< 0.1%

Interactions

2025-03-11T13:37:23.657134image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:16.157939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:17.333787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.189485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.993253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:19.841167image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.650485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:21.732456image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:22.595936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:23.751782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:16.269439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:17.423477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.283147image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:19.074932image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:19.940596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.739654image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:21.825267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:22.695662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:23.834652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:16.357465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:17.503074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.370649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:19.154998image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.025271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.825512image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:21.913649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:22.799260image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:23.929901image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:16.495860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:17.621612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.465150image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:19.252303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.114991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.917737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:22.006939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:22.900556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:24.050251image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:16.586465image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:17.711176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.546902image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:19.355868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.195180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.997683image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:22.089477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:22.997406image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:24.153509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:16.873803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:17.816602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.640658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:19.484916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.292895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:21.086293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:22.195038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:23.098891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:24.244573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:17.051986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:17.933090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.733054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:19.588206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.384269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:21.427724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:22.303759image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:23.183767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:24.335177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:17.139104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.018924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.819996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:19.667595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.468220image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:21.522760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:22.400713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:23.383599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:24.508282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:17.242454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.106730image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:18.908197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:19.764008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:20.558686image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:21.615040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:22.498613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-11T13:37:23.559476image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-03-11T13:37:28.973717image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
age_of_first_empcb_person_cred_hist_lengthcb_person_default_on_fileidloan_amntloan_gradeloan_int_rateloan_intentloan_percent_incomeloan_statusperson_ageperson_emp_lengthperson_home_ownershipperson_income
age_of_first_emp1.0000.5560.0270.013-0.0340.0450.0580.0590.0190.0590.647-0.6210.089-0.064
cb_person_cred_hist_length0.5561.0000.0110.0170.0400.022-0.0010.078-0.0320.0330.8030.0540.0410.097
cb_person_default_on_file0.0270.0111.0000.0240.0760.6210.5750.0280.0800.2110.0000.0560.0990.020
id0.0130.0170.0241.000-0.1010.037-0.0640.034-0.1270.1360.0070.0030.0290.042
loan_amnt-0.0340.0400.076-0.1011.0000.0910.1480.0330.6790.2260.0680.1120.0690.377
loan_grade0.0450.0220.6210.0370.0911.0000.7240.0550.0850.4950.0160.0290.1370.000
loan_int_rate0.058-0.0010.575-0.0640.1480.7241.0000.0520.1800.4400.003-0.0870.123-0.060
loan_intent0.0590.0780.0280.0340.0330.0550.0521.0000.0260.2160.0820.0260.0830.000
loan_percent_income0.019-0.0320.080-0.1270.6790.0850.1800.0261.0000.440-0.036-0.0670.100-0.327
loan_status0.0590.0330.2110.1360.2260.4950.4400.2160.4401.0000.0420.0990.2800.031
person_age0.6470.8030.0000.0070.0680.0160.0030.082-0.0360.0421.0000.0990.0390.145
person_emp_length-0.6210.0540.0560.0030.1120.029-0.0870.026-0.0670.0990.0991.0000.1570.233
person_home_ownership0.0890.0410.0990.0290.0690.1370.1230.0830.1000.2800.0390.1571.0000.041
person_income-0.0640.0970.0200.0420.3770.000-0.0600.000-0.3270.0310.1450.2330.0411.000

Missing values

2025-03-11T13:37:24.639765image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-11T13:37:24.861558image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idperson_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_statusage_of_first_emp
772120000RENT0.0PERSONALC250013.490.13Y3021.0
10103078000MORTGAGE5.0VENTUREB1280010.590.17N5025.0
15152933000OWN8.0MEDICALA73008.900.23N8021.0
32323080000RENT3.0MEDICALB600010.750.07N8027.0
37372268000MORTGAGE7.0PERSONALC1590013.490.25N2015.0
38383054000RENT0.0MEDICALB1250011.710.24N10130.0
62622860000RENT5.0PERSONALE1720017.060.28Y8023.0
68683162900MORTGAGE2.0MEDICALD1800014.090.24N5129.0
69692440000RENT3.0MEDICALC300013.220.07N4021.0
73732424000RENT4.0MEDICALB300010.950.13N2020.0
idperson_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_statusage_of_first_emp
58492584922185000RENT5.0PERSONALB100009.990.12N4016.0
58498584982830000RENT0.0MEDICALA30007.490.10N8028.0
58515585152230000RENT7.0EDUCATIONA30007.140.10N2015.0
58532585322854000MORTGAGE5.0VENTUREE500016.820.09N7023.0
58546585462735000RENT3.0VENTUREC300013.220.09N5024.0
58549585492265000RENT3.0PERSONALA150006.540.23N3019.0
58563585632854000RENT4.0DEBTCONSOLIDATIONE1500016.350.28N9124.0
58611586112124000RENT5.0MEDICALC1000013.850.42N4116.0
58621586212590400MORTGAGE9.0DEBTCONSOLIDATIONA95008.900.11N4016.0
586405864034120000MORTGAGE5.0EDUCATIOND2500015.950.21Y10029.0